We use a batch-size of 32 for both training and development set, with a random chunk of 3 seconds obtained for each sample per training epoch. A learning rate of 0.01 is applied with the ADAM optimizer. The cross entropy loss [1]} is used as the objective function to optimize the model. Each fitted model is trained for 50 epochs and the best model (with lowest loss) on the development set is saved.
